Local Fractional Order Derivative Vector Quantization Pattern for Face Recognition

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• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10113
• 期：1
• 页码：234-247
• 丛书名：Computer Vision ? ACCV 2016
• ISBN：978-3-319-54187-7
• 卷排序：10113

文摘

Previous works have shown that fractional order derivative can give a better image description compared with conventional integral one in applications of edge detection, image segmentation, image restoration, and so on. Motivated by this conclusion, in this paper, we propose a novel local image descriptor, local fractional order derivative vector quantization pattern (fVQP), based on image local directional fractional order derivative feature vector and vector quantization method for face recognition. Compared with image integral order derivative information based local binary pattern (LBP), local derivative pattern (LDP) and local directional derivative pattern (LDDP), our fVQP image descriptor has the advantages of better image recognition performance and robust to noise. Extensive experimental results conducted on four benchmark face databases demonstrate the superior performance of our fVQP compared with existing state-of-the-art descriptors for face recognition in terms of recognition rate.